LSH Seminar Stef Maree (LSH)

Title: Solving multi-modal optimization problems using Estimation of Distribution Algorithms

Abstract: Estimation of Distribution Algorithms (EDAs) are heuristic optimization algorithms that try to iteratively find better solutions to a given (black-box) problem. Each iteration, new candidate solutions are sampled from a probability distribution. EDAs equipped with a Gaussian distribution have shown to be successful in real-valued optimization. However, performance often deteriorates when the problem at hand is multi-model, as multiple modes in the fitness landscape have to be modelled with a unimodal Gaussian. In this presentation, we focus on models that can adapt to the multi-modality of the fitness landscape. Specifically, we discuss Hill-Valley Clustering, a remarkably simple approach to adaptively cluster the search space in niches, such that a single mode resides in each niche. In each of the located niches, an EDA is initialized to optimize that niche. Combined with an EDA and a restart scheme, the resulting Hill-Valley Evolutionary Algorithm (HillVallEA) is, even though its remarkable simplicity competitive to the state-of-the-art algorithms and shows superior performance in the long run.